Results for “ai-safety”

Analyze text and images for harmful content using Azure AI Content Safety, with customizable blocklists and severity thresholds.

Apply least-privilege tool allowlisting, identity binding, and human-in-the-loop controls for agent tool calls.

Scan and audit AI agent skills for security risks before installation, producing a PASS/WARN/FAIL verdict with findings and remediation guidance.

Assess AI/ML systems for prompt injection, jailbreak vulnerabilities, model inversion risk, data poisoning exposure, and agent tool abuse, with MITRE ATLAS mapping and guardrail recommendations.

Authorized security assessment of REST, GraphQL, WebSocket, and SOAP APIs covering discovery, authentication, authorization, rate-limiting, and CI/CD integration.

Conduct authorized security assessments of LLM applications and AI agents, covering prompt injection, tool abuse, RAG exposure, memory poisoning, and model supply-chain risks.

Expert guidance on secure coding practices, focusing on preventing common security errors made by AI during code generation. Use for: auditing AI-generated code, implementing secure design patterns, and ensuring code follows OWASP Top 10 standards. Covers input validation, authentication, cryptography, file handling, secure configuration, and business logic security.

Safety guardrails for autonomous agents. Intercepts destructive actions and vets skills, requires structured approval before irreversible operations, with CLI-backed cryptographic audit trails and policy enforcement.

Analyze text and images for harmful content using Azure AI Content Safety SDK for Java. Supports hate, violence, sexual content, and self-harm detection with blocklist management.

Identifies error-prone APIs, dangerous configurations, and footgun designs that enable security mistakes in API designs, configuration schemas, and cryptographic library ergonomics.

Check any AI agent codebase against the OWASP Agentic Security Initiative (ASI) Top 10 risks, scanning for controls and generating a compliance report.

AI security and safety specialist for guardrails and adversarial resilience. Use when the user asks to talk to Snape, requests a safety audit, or needs guardrails design.

Generates, stores, validates, rotates, and revokes API keys with secure hashing, scoping, rate limiting, and leak monitoring.

Configure Google Apigee security policies including JSON/XML threat protection, OAuth 2.0, SpikeArrest, and Advanced API Security to defend against OWASP API Top 10 threats.

Classifies AI systems under the EU AI Act's risk tiers and maps compliance obligations for conformity, transparency, and human oversight.

An API has no UI to hide things behind.

Protect API Gateway endpoints with AWS WAF by configuring managed rule groups, rate limiting, bot control, IP reputation filtering, and monitoring.

Builds input and output validation guardrails for LLM-powered applications to prevent prompt injection, data leakage, toxic content generation, and hallucinated outputs using NeMo Guardrails, Presidio, and Guardrails AI.

Bagman

Store and retrieve API keys, passwords, and credentials in an HSM-backed encrypted vault via MCP or REST API, with policy-based access control, secret rotation, multi-chain signing, and prompt injection scanning.

Continuously discover, classify, and score APIs based on risk while enforcing security policies across the API lifecycle.

Implements secure API design patterns including authentication, authorization, input validation, rate limiting, and protection against common vulnerabilities.

Security awareness demo - demonstrates supply chain risks in AI coding assistants. See the full research: https://x.com/theonejvo/status/2015892980851474595

Security engineer skill for backend ACL structure, menu visibility control, and administrative access safety.

Create and manage Ethereum wallets, including EOAs, smart contract wallets, Safe multisig, EIP-7702, and account abstraction, with AI agent key safety rules and transaction approval thresholds.

Orchestrates Gemini 3 Flash to audit Linux Kernel patches for memory safety, detect cost drifts in Terraform plans, and generate hardened Kubernetes security contexts.